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Rapid Deployment of Anomaly Detection Models for Large Number of Emerging KPI Streams | IEEE Conference Publication | IEEE Xplore

Rapid Deployment of Anomaly Detection Models for Large Number of Emerging KPI Streams


Abstract:

Internet-based services monitor and detect anomalies on KPIs (Key Performance Indicators, say CPU utilization, number of queries per second, response latency) of their ap...Show More

Abstract:

Internet-based services monitor and detect anomalies on KPIs (Key Performance Indicators, say CPU utilization, number of queries per second, response latency) of their applications and systems in order to keep their services reliable. This paper identifies a common, important, yet little-studied problem of KPI anomaly detection: rapid deployment of anomaly detection models for large number of emerging KPI streams, without manual algorithm selection, parameter tuning, or new anomaly labeling for any newly emerging KPI streams. We propose the first framework ADS (Anomaly Detection through Self-training) that tackles the above problem, via clustering and semi-supervised learning. Our extensive experiments using real-world data show that, with the labels of only the 5 cluster centroids of 70 historical KPI streams, ADS achieves an averaged best F-score of 0.92 on 81 new KPI streams, almost the same as a state-of-art supervised approach, and greatly outperforming a state-of-art unsupervised approach by 61.40% on average.
Date of Conference: 17-19 November 2018
Date Added to IEEE Xplore: 13 May 2019
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Conference Location: Orlando, FL, USA

References

References is not available for this document.